# Exploring Kinetic Curves Features for the Classification of Benign and Malignant Breast Lesions in DCE-MRI

arXiv: https://arxiv.org/abs/2404.13929 · HTML: https://mlsanity.com/paper/2404.13929/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound](https://mlsanity.com/paper/1907.01743/) (2019)
- [Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation](https://mlsanity.com/paper/2210.10956/) (2022)
- [SimPLe: Similarity-Aware Propagation Learning for Weakly-Supervised Breast Cancer Segmentation in DCE-MRI](https://mlsanity.com/paper/2306.16714/) (2023)
- [Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation](https://mlsanity.com/paper/2201.00317/) (2022)

## Built on by (most cited first, 1 of 1)

- [Transformer Classification of Breast Lesions: The BreastDCEDL_AMBL Benchmark Dataset and 0.92 AUC Baseline](https://mlsanity.com/paper/2509.26440/) (2025)

## Abstract (arXiv)

Breast cancer is the most common malignant tumor among women and the second cause of cancer-related death. Early diagnosis in clinical practice is crucial for timely treatment and prognosis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has revealed great usability in the preoperative diagnosis and assessing therapy effects thanks to its capability to reflect the morphology and dynamic characteristics of breast lesions. However, most existing computer-assisted diagnosis algorithms only consider conventional radiomic features when classifying benign and malignant lesions in DCE-MRI. In this study, we propose to fully leverage the dynamic characteristics from the kinetic curves as well as the radiomic features to boost the classification accuracy of benign and malignant breast lesions. The proposed method is a fully automated solution by directly analyzing the 3D features from the DCE-MRI. The proposed method is evaluated on an in-house dataset including 200 DCE-MRI scans with 298 breast tumors (172 benign and 126 malignant tumors), achieving favorable classification accuracy with an area under curve (AUC) of 0.94. By simultaneously considering the dynamic and radiomic features, it is beneficial to effectively distinguish between benign and malignant breast lesions. The algorithm is publicly available at https://github.com/ryandok/JPA.